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    <title>Hive性能优化之ORC索引–Row Group Index vs Bloom Filter Index_大数据_liu1390910的博客-CSDN博客</title>
    <meta name="description" content="转:Hive性能优化之ORC索引–Row Group Index vs Bloom Filter Index之前的文章《更高的压缩比，更好的性能–使用ORC文件格式优化Hive》中介绍了Hive的ORC文件格式，它不但有着很高的压缩比，节省存储和计算资源之外，还通过一个内置的轻量级索引，提升查询的性能。这个内置的轻量级索引，就是下面所说的Row Group Index。其实ORC支持的索引不止这一...大数据">
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                <h1 class="title-article">Hive性能优化之ORC索引–Row Group Index vs Bloom Filter Index</h1>
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                                <div class="up-time"><span>最后发布:2018-02-27 09:44:00</span><span>首发:2018-02-27 09:44:00</span></div>
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                                            <div class="postTitle" style="font-size:14.7px;font-weight:bold;color:rgb(75,75,75);font-family:Verdana, Arial, Helvetica, sans-serif;"><h1 style="font-size:14.7px;color:rgb(102,102,102);"><a class="postTitle2" href="http://www.cnblogs.com/wujin/p/6405355.html" rel="nofollow" style="color:rgb(52,104,164);">转:Hive性能优化之ORC索引–Row Group Index vs Bloom Filter Index</a></h1></div><div class="blogpost-body" style="color:rgb(75,75,75);font-family:Verdana, Arial, Helvetica, sans-serif;font-size:13px;line-height:20.7999992370605px;"><p>之前的文章《<a title="" href="http://lxw1234.com/archives/2016/04/630.htm" rel="nofollow" style="color:rgb(52,104,164);">更高的压缩比，更好的性能–使用ORC文件格式优化Hive</a>》中介绍了Hive的ORC文件格式，它不但有着很高的压缩比，节省存储和计算资源之外，还通过一个内置的轻量级索引，提升查询的性能。这个内置的轻量级索引，就是下面所说的Row Group Index。</p><p>其实ORC支持的索引不止这一种，还有一种BloomFilter索引，两者结合起来，更加提升了Hive中基于ORC的查询性能。</p><p>说明一下：本文使用<strong>Hive2.0.0 + hadoop-2.3.0-cdh5.0.0</strong>作为测试环境。表lxw1234_text为text格式保存，总记录数为12000920。</p><h2 style="font-size:21px;line-height:1.5;"><span style="font-size:16px;">Row Group Index</span></h2><p>由之前的文章知道，一个ORC文件包含一个或多个<strong>stripes(groups of row data)，每个stripe中包含了每个column的min/max值的索引数据，当查询中有&lt;,&gt;,=的操作时，会根据min/max值，跳过扫描不包含的stripes。</strong></p><p>而其中为每个stripe建立的包含min/max值的索引，就称为Row Group Index，也叫min-max Index，或者Storage Index。在建立ORC格式表时，指定表参数’orc.create.index’=’true’之后，便会建立Row Group Index，需要注意的是，为了使Row Group Index有效利用，向表中加载数据时，必须对需要使用索引的字段进行排序，否则，min/max会失去意义。另外，这种索引通常用于<strong>数值型字段</strong>的查询过滤优化上。</p><p>看下面的例子：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">CREATE TABLE lxw1234_orc2 stored AS ORC</span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">TBLPROPERTIES</span></li><li class="L2" style="font-size:12px;"><span class="pun" style="font-size:16px;">(<span class="str">'orc.compress'<span class="pun">=<span class="str">'SNAPPY'<span class="pun">,</span></span></span></span></span></li><li class="L3" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.create.index'<span class="pun">=<span class="str">'true'<span class="pun">,</span></span></span></span></li><li class="L4" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.bloom.filter.fpp'<span class="pun">=<span class="str">'0.05'<span class="pun">,</span></span></span></span></li><li class="L5" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.stripe.size'<span class="pun">=<span class="str">'10485760'<span class="pun">,</span></span></span></span></li><li class="L6" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.row.index.stride'<span class="pun">=<span class="str">'10000'<span class="pun">)</span></span></span></span></li><li class="L7" style="font-size:12px;"><span class="pln" style="font-size:16px;">AS</span></li><li class="L8" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT CAST<span class="pun">(<span class="pln">siteid AS INT<span class="pun">)<span class="pln"> AS id<span class="pun">,</span></span></span></span></span></span></li><li class="L9" style="font-size:12px;"><span class="pln" style="font-size:16px;">pcid</span></li><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">FROM lxw1234_text</span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">DISTRIBUTE BY id sort BY id<span class="pun">;</span></span></li></ol><p>直接执行下面的查询（未使用索引）：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT COUNT<span class="pun">(<span class="lit">1<span class="pun">)<span class="pln"> FROM lxw1234_orc1 WHERE id <span class="pun">&gt;=<span class="pln"> <span class="lit">1382<span class="pln"> AND id <span class="pun">&lt;=<span class="pln"> <span class="lit">1399<span class="pun">;</span></span></span></span></span></span></span></span></span></span></span></span></span></li></ol><p><img class="aligncenter" src="http://7xipth.com1.z0.glb.clouddn.com/20160407-1.jpg" alt="orc index" width="980" height="487" style="border:0px;" /></p><p>很明显，扫描了所有记录。再使用索引查询：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="kwd" style="font-size:16px;">set<span class="pln"> hive<span class="pun">.<span class="pln">optimize<span class="pun">.<span class="pln">index<span class="pun">.<span class="pln">filter<span class="pun">=<span class="kwd">true<span class="pun">;</span></span></span></span></span></span></span></span></span></span></span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT COUNT<span class="pun">(<span class="lit">1<span class="pun">)<span class="pln"> FROM lxw1234_orc1 WHERE id <span class="pun">&gt;=<span class="pln"> <span class="lit">1382<span class="pln"> AND id <span class="pun">&lt;=<span class="pln"> <span class="lit">1399<span class="pun">;</span></span></span></span></span></span></span></span></span></span></span></span></span></li></ol><p><img class="aligncenter" src="http://7xipth.com1.z0.glb.clouddn.com/20160407-2.jpg" alt="orc index" width="1003" height="486" style="border:0px;" /></p><p>可以看到，只扫描了部分记录，即根据Row Group Index中的min/max跳过了WHERE条件中不包含的stripes，索引有效果。</p><p>假如有下面的查询：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">SET hive<span class="pun">.<span class="pln">optimize<span class="pun">.<span class="pln">index<span class="pun">.<span class="pln">filter<span class="pun">=<span class="kwd">true<span class="pun">;</span></span></span></span></span></span></span></span></span></span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT COUNT<span class="pun">(<span class="lit">1<span class="pun">)<span class="pln"> FROM lxw1234_orc1 WHERE id <span class="pun">&gt;=<span class="pln"> <span class="lit">0<span class="pln"> AND id <span class="pun">&lt;=<span class="pln"> <span class="lit">1000</span></span></span></span></span></span></span></span></span></span></span></span></li><li class="L2" style="font-size:12px;"><span class="pln" style="font-size:16px;">AND pcid IN <span class="pun">(<span class="str">'0005E26F0DCCDB56F9041C'<span class="pun">,<span class="str">'A'<span class="pun">);</span></span></span></span></span></span></li></ol><p>执行的过程大概是这样的：</p><p>先根据Row Group Index中的min/max，判断哪些stripes/file包含在内，接着逐行扫描，过滤pcid IN (‘0005E26F0DCCDB56F9041C’,’A’)的记录。</p><p><img class="aligncenter" src="http://7xipth.com1.z0.glb.clouddn.com/20160407-3.jpg" alt="orc index" width="1160" height="496" style="border:0px;" /></p><p>可以看到，没有全表扫描，跳过了一部分stripes。这样看来，如果where后面的id范围很大，完全可能会包含所有的文件，再根据pcid过滤时候，又相当于全表扫描了。</p><p>对于这种查询场景的优化策略，就是下面的BloomFilter索引。</p><h2 style="font-size:21px;line-height:1.5;"><span style="font-size:16px;">Bloom Filter Index</span></h2><p>之前有篇文章《<a title="" href="http://lxw1234.com/archives/2015/12/580.htm" rel="nofollow" style="color:rgb(52,104,164);">大数据去重统计之BloomFilter</a>》，介绍过BloomFilter的原理和Java版的例子。Hive的ORC中基于此，提供了BloomFilter索引，用于性能优化。</p><p>在建表时候，通过表参数”orc.bloom.filter.columns”=”pcid”来指定为那些字段建立BloomFilter索引，这样，在生成数据的时候，会在每个stripe中，为该字段建立BloomFilter的数据结构，当查询条件中包含对该字段的=号过滤时候，先从BloomFilter中获取以下是否包含该值，如果不包含，则跳过该stripe.</p><p>看下面的建表语句，为pcid字段建立BloomFilter索引：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">CREATE TABLE lxw1234_orc2 stored AS ORC</span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">TBLPROPERTIES</span></li><li class="L2" style="font-size:12px;"><span class="pun" style="font-size:16px;">(<span class="str">'orc.compress'<span class="pun">=<span class="str">'SNAPPY'<span class="pun">,</span></span></span></span></span></li><li class="L3" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.create.index'<span class="pun">=<span class="str">'true'<span class="pun">,</span></span></span></span></li><li class="L4" style="font-size:12px;"><span class="str" style="font-size:16px;">"orc.bloom.filter.columns"<span class="pun">=<span class="str">"pcid"<span class="pun">,</span></span></span></span></li><li class="L5" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.bloom.filter.fpp'<span class="pun">=<span class="str">'0.05'<span class="pun">,</span></span></span></span></li><li class="L6" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.stripe.size'<span class="pun">=<span class="str">'10485760'<span class="pun">,</span></span></span></span></li><li class="L7" style="font-size:12px;"><span class="str" style="font-size:16px;">'orc.row.index.stride'<span class="pun">=<span class="str">'10000'<span class="pun">)</span></span></span></span></li><li class="L8" style="font-size:12px;"><span class="pln" style="font-size:16px;">AS</span></li><li class="L9" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT CAST<span class="pun">(<span class="pln">siteid AS INT<span class="pun">)<span class="pln"> AS id<span class="pun">,</span></span></span></span></span></span></li><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">pcid</span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">FROM lxw1234_text</span></li><li class="L2" style="font-size:12px;"><span class="pln" style="font-size:16px;">DISTRIBUTE BY id sort BY id<span class="pun">;</span></span></li></ol><p>然后执行上面的查询：</p><ol class="linenums"><li class="L0" style="font-size:12px;"><span class="pln" style="font-size:16px;">SET hive<span class="pun">.<span class="pln">optimize<span class="pun">.<span class="pln">index<span class="pun">.<span class="pln">filter<span class="pun">=<span class="kwd">true<span class="pun">;</span></span></span></span></span></span></span></span></span></span></li><li class="L1" style="font-size:12px;"><span class="pln" style="font-size:16px;">SELECT COUNT<span class="pun">(<span class="lit">1<span class="pun">)<span class="pln"> FROM lxw1234_orc1 WHERE id <span class="pun">&gt;=<span class="pln"> <span class="lit">0<span class="pln"> AND id <span class="pun">&lt;=<span class="pln"> <span class="lit">1000</span></span></span></span></span></span></span></span></span></span></span></span></li><li class="L2" style="font-size:12px;"><span class="pln" style="font-size:16px;">AND pcid IN <span class="pun">(<span class="str">'0005E26F0DCCDB56F9041C'<span class="pun">,<span class="str">'A'<span class="pun">);</span></span></span></span></span></span></li></ol><p><img class="aligncenter" src="http://7xipth.com1.z0.glb.clouddn.com/20160407-5.jpg" alt="orc index" width="897" height="476" style="border:0px;" /></p></div>                                    </div>
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				在这篇博客中，我们将一起分析通过Spark访问Hive的数据，主要分享以下几点内容：1.      如何通过Spark Shell交互式访问Spark2.      如何读取HDFS文件和创建一个RD...			</a>
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